This paper describes some of the main opportunities and challenges of using AI in healthcare. It then turns to a case study of the use of AI for healthcare purposes in India, discussing key applications, challenges and risks in this context.
Inside view of SigTuple office in Bengaluru, on 3 May 2017. The company is helping hospitals and healthcare centres to improve the speed and accuracy of blood reports. Photo by Mint/Getty Images.
- AI, the use of coded computer software routines with specific instructions to perform tasks for which a human brain is considered necessary, is providing the healthcare sector with new advances that are being hailed as game changers.
- The risk and challenges of integrating AI into healthcare are closely related to the use of the data needed to feed AI systems. Issues around quality, safety, governance, privacy, consent and ownership must all be properly addressed. A lack of explainability, as it is almost impossible to understand how AI arrived at a specific decision, also points to a potential lack of trust in AI systems.
- India provides a case study of how a country is actively promoting the use of AI to address healthcare needs. However, the deployment of AI in India is still at a very nascent stage, particularly for clinical interventions.
- The challenge of delivering quality healthcare at scale presents a strong case for developing AI-based solutions for healthcare in India. However, a complex health landscape involving numerous stakeholders, competing priorities, entrenched incentive systems and institutional cultures give rise to a range of challenges and risks across the stages of development, adoption and deployment.
- The quality of digital infrastructure, affordability, and variable capacity among states and medical professionals are together likely to result in adoption of AI applications primarily by India’s well-established private hospitals. This in turn could result in new inequities in access to quality healthcare.
- The effectiveness of AI systems will depend on accurate problem identification and solution matching. Currently, there is a risk that solutions are being technology-led rather than problem-led, and as a result are often blind to particular contextual needs or constraints.
Source: International Law and Governance